2.11

R Markdown

library(tidyverse)
library(plotly)

mortality <- read_csv("mortality.csv")

g <- mortality %>% 
  filter(Entity %in%  c('Kazakhstan','Colombia','Guatemala')) %>% 
  ggplot(aes(x = Day,
             y = p_avg_all_ages,
             group = factor(Entity, levels = c("Kazakhstan", "Guatemala", "Colombia")),
             color = Entity,
             #text = sprintf("test")
             )) +
  
  #Guide Lines
  
  geom_hline(yintercept = 0, color = 'gray', alpha = 0.6) +
  geom_hline(yintercept = c(-50,50,100), color = 'gray',linetype = 2, alpha = 0.4) +
  coord_cartesian(ylim = c(-55, 140)) +
  scale_x_date(date_breaks = "316 days",
               date_labels =  "%b %d, %Y",
               #limits = c(as.Date("2019-12-31"), NA)
               ) +
  scale_y_continuous(labels = function(x) paste0(x, "%")) +
  
  #Real Lines
  
  geom_line() +
  geom_point() +
  
  #Theme lines
  
  scale_color_manual(values = c("#911010", "#42318c", '#278265')) +
  theme(axis.line = element_line(colour = "white"),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_blank(),
        panel.background = element_blank()) +
  
  #Legend
  
  theme(legend.position = 'right') +
  theme(text = element_text(family="serif"),
        plot.caption = element_text(hjust = 0, size = 7),
        legend.title = element_blank(),
        axis.text = element_text(size = 12),
        axis.title = element_text(size = 14,face = "bold"),
        #plot.title = element_text(hjust = 0),
        #plot.subtitle = element_text(hjust = 0)
        ) +
  scale_fill_discrete(limits = c("Kazakhstan", "Guatemala", "Colombia")) +

  #DOESN'T WORK
  
  guides(fill = guide_legend(reverse = TRUE)) +
  
  #Subtitle
  
  labs(caption = 'Source: Human Mortality Database (2022), World Mortality Dataset (2022)
Note: Comparisons across countries are affected by differences in the completeness of death reporting. Details can
be found at our Excess Mortality page.',
       title = str_wrap('Excess mortality: Deaths from all causes compared to average over previous years', 65),
       subtitle = str_wrap('The percentage difference between the reported number of weekly or monthly deaths in 2020–2022 and the average number of deaths in the same period over the years 2015–2019. The reported number might not count all deaths that occurred due to incomplete coverage and delays in reporting.', 85),
       x = '',y = '') 
g

ggplotly(g, tooltip = c("Day",'p_avg_all_ages')) %>%
  layout(hovermode = "x unified")